Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

ArXi:2605.26759v1 Announce Type: new Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typically rely on dataset-specific optimization, making it difficult to transfer their causal discovery capabilities to new time series governed by diverse causal mechanisms. In this paper, we propose \textbf{PTCD}, a novel \textbf{P}re